Semantic Information Oriented No-Reference Video Quality Assessment

In this letter, a method called Semantic Information Oriented No-Reference (SIONR) video quality assessment model is developed, which can effectively represent quality degradation of video by taking the variations of semantic information into consideration. Specially, temporal variations of the semantic features between adjacent frames are calculated to consider the inconsistency of the static semantic information. Moreover, low-level features are also applied as a supplementary to take distortions related to local details into consideration. Experimental results demonstrate that our proposed method obtains competitive performance compared with state-of-the-art methods in the two databases. Also, our model achieves good generalization capability. The code is available at: https://github.com/lorenzowu/SIONR.

Automated summary

In this letter, a new video quality assessment model called SIONR is proposed, which effectively represents quality degradation by considering semantic information variations. The model achieves competitive performance compared to state-of-the-art methods in two databases and demonstrates good generalization capability.